Computer-assisted minimally invasive surgery such as endoscopy has grown in popularity over the past years. However, due to the nature of these procedures, issues like limited field-of-view, extreme lighting conditions, lack of depth information, and difficulty in manipulating operating instruments demand strenuous amounts of effort from the surgeons [1]}. Surgical data science applications could provide physicians with context-aware assistance during minimally invasive surgery to overcome these limitations and increase patient safety. One of the main forms of assistance is providing accurate tracking of medical instruments using instance segmentation methods. These systems are expected to be a crucial component in tasks ranging from surgical navigation, skill analysis, complication prediction, and other computer-integrated surgery (CIS) applications [2]}.
